Multi-Voxel Pattern Analysis of fMRI Data
نویسندگان
چکیده
The central goal of cognitive neuroscience is to understand how information is processed in the brain. To accomplish this goal, researchers studying human cognition are increasingly relying on multi-voxel pattern analysis (MVPA); this method involves analyzing spatially distributed (multi-voxel) patterns of functional MRI activity, with the goal of decoding the information that is represented across the ensemble of voxels. In this chapter, we describe the major subtypes of MVPA, we provide examples of how MVPA has been used to study neural information processing, and we highlight recent technical advances in MVPA.
منابع مشابه
Voxel Selection of fMRI Data using Multi- Voxel Pattern Analysis to Predict Neural Response
fMRI system shows that all the information of the brain that is represented in the subject of the brain at a particular point in time. The MVPA approach has lead to several impressive facts of brain reading. fMRI data uses Multivoxel pattern analysis (MVPA) approach to relate the neural activities to cognition. A challenging factor is to build a generalizable classification model because the nu...
متن کاملCombined MEG and fMRI model
An integrated model for magnetoencephalography (MEG) and functional Magnetic Resonance Imaging (fMRI) is proposed. In the proposed model, MEG and fMRI outputs are related to the corresponding aspects of neural activities in a voxel. Post synaptic potentials (PSPs) and action potentials (APs) are two main signals generated by neural activities. In the model, both of MEG and fMRI are related to t...
متن کاملMapping Informative Clusters in a Hierarchial Framework of fMRI Multivariate Analysis
Pattern recognition methods have become increasingly popular in fMRI data analysis, which are powerful in discriminating between multi-voxel patterns of brain activities associated with different mental states. However, when they are used in functional brain mapping, the location of discriminative voxels varies significantly, raising difficulties in interpreting the locus of the effect. Here we...
متن کاملThe advantage of brief fMRI acquisition runs for multi-voxel pattern detection across runs
Functional magnetic resonance imaging (fMRI) studies are broken up into runs (or 'sessions'), frequently selected to be long to minimize across-run signal variations. For investigations that use multi-voxel pattern analysis (MVPA), however, employing many short runs might improve a classifier's ability to generalize across irrelevant pattern variations and detect condition-related activity patt...
متن کاملSearchlight-based multi-voxel pattern analysis of fMRI by cross-validated MANOVA
Multi-voxel pattern analysis (MVPA) is a fruitful and increasingly popular complement to traditional univariate methods of analyzing neuroimaging data. We propose to replace the standard 'decoding' approach to searchlight-based MVPA, measuring the performance of a classifier by its accuracy, with a method based on the multivariate form of the general linear model. Following the well-established...
متن کاملInformational connectivity: identifying synchronized discriminability of multi-voxel patterns across the brain
The fluctuations in a brain region's activation levels over a functional magnetic resonance imaging (fMRI) time-course are used in functional connectivity (FC) to identify networks with synchronous responses. It is increasingly recognized that multi-voxel activity patterns contain information that cannot be extracted from univariate activation levels. Here we present a novel analysis method tha...
متن کامل